Disclosed is a way to provide a medical image processing device that may include a hardware processor that calculates at least one of trabecular connectivity, trabecular width, trabecular number, mineralization degree, osteoid volume, cortical width, and cortical porosity as a bone characteristic indicator of a subject from reconstructed image data generated from moiré image data acquired by photographing the subject.
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1. A medical image processing device, comprising: a hardware processor that (i) calculates at least one of trabecular connectivity, trabecular width, trabecular number, mineralization degree, osteoid volume, cortical width, and cortical porosity as a bone characteristic indicator of a subject from reconstructed image data generated from moiré image data acquired by photographing the subject, (ii) evaluates a risk of the subject's bone having a fracture based on the at least one calculation, (iii) calculates bone density or bone mineral volume of the subject from the reconstructed image data, and (iv) calculates a bone strength indicator based on an addition involving the calculated bone characteristic indicator, the calculated bone density or bone mineral volume, and a weight coefficient; and a display that displays at least one of the bone characteristic indicator and the bone strength indicator.
This invention relates to a medical image processing system for assessing bone health and fracture risk. The device analyzes moiré image data, which is a type of interference pattern imaging, to reconstruct detailed bone images. From this data, it calculates various bone characteristics, including trabecular connectivity, trabecular width, trabecular number, mineralization degree, osteoid volume, cortical width, and cortical porosity. These metrics help assess bone microarchitecture and structural integrity. The system also evaluates fracture risk by analyzing these bone characteristics and computes bone density or bone mineral volume. A key feature is the calculation of a bone strength indicator, which combines the bone characteristic metrics, bone density, and a weight coefficient to provide a comprehensive assessment of bone strength. The results are displayed for medical professionals, enabling better diagnosis and treatment planning. This approach improves upon traditional bone density measurements by incorporating microstructural analysis, offering a more accurate prediction of fracture risk. The system is designed to enhance diagnostic precision in osteoporosis and other bone-related conditions.
2. The medical image processing device according to claim 1 , wherein the hardware processor corrects the bone characteristic indicator based on at least one of a photographed body part of the subject and a photographing condition.
This invention relates to medical image processing, specifically improving the accuracy of bone characteristic indicators in medical imaging. The problem addressed is the variability in bone measurements due to differences in body parts being imaged and photographing conditions, which can lead to inconsistent diagnostic results. The device includes a hardware processor that processes medical images to extract bone characteristics, such as density or structure, and corrects these indicators based on the specific body part being photographed (e.g., femur, spine) and the imaging conditions (e.g., exposure settings, patient positioning). By adjusting the bone characteristic indicators according to these factors, the device ensures more reliable and standardized measurements, enhancing diagnostic accuracy. The correction process may involve applying predefined correction factors or algorithms tailored to different body parts and imaging conditions. This approach reduces errors caused by inconsistencies in imaging protocols, improving the consistency and reliability of bone assessments in clinical settings.
3. The medical image processing device according to claim 1 , wherein the hardware processor corrects the bone strength indicator based on at least one of a photographed body part of the subject and a photographing condition.
This invention relates to medical image processing devices that analyze bone strength indicators from medical images, such as X-rays or CT scans. The device processes these images to assess bone health, which is critical for diagnosing conditions like osteoporosis or fractures. A key challenge in such systems is ensuring accurate bone strength measurements, as factors like the body part being imaged or the imaging conditions can introduce errors. The device includes a hardware processor that corrects the bone strength indicator based on at least one of the photographed body part or the photographing conditions. For example, if the image is of a femur, the processor may adjust the bone strength calculation to account for differences in bone density compared to other bones. Similarly, if the imaging conditions, such as exposure settings or patient positioning, vary, the processor compensates for these variations to maintain accuracy. This correction ensures that the bone strength indicator is reliable and consistent across different imaging scenarios, improving diagnostic accuracy. The device may also include additional features, such as image preprocessing to enhance clarity or machine learning models to refine bone strength predictions. By addressing these variables, the system provides more dependable bone health assessments for clinical use.
4. The medical image processing device according to claim 1 , wherein the hardware processor calculates the bone strength indicator for each of a plurality of small regions acquired by dividing a measurement target region of the reconstructed image data, and calculates a statistic of a plurality of calculated bone strength indicators.
This invention relates to medical image processing, specifically for analyzing bone strength from reconstructed image data. The technology addresses the challenge of accurately assessing bone fragility or strength, which is critical for diagnosing conditions like osteoporosis or evaluating fracture risk. The device processes medical imaging data, such as from CT or MRI scans, to generate a bone strength indicator for a measurement target region. The hardware processor divides this region into multiple small regions, calculates a bone strength indicator for each, and then computes a statistic (e.g., mean, median, or standard deviation) of these indicators. This approach provides a more detailed and localized assessment of bone strength compared to traditional methods that analyze the entire region uniformly. The invention improves diagnostic accuracy by accounting for variations in bone density and structure across different parts of the target area. The system may also include preprocessing steps like noise reduction or image enhancement to ensure reliable strength calculations. The output can be used to generate visual representations or numerical values for clinical evaluation. This method enhances the precision of bone health assessments, supporting better treatment decisions.
5. The medical image processing device according to claim 1 , wherein the hardware processor calculates trabecular anisotropy of the subject from the reconstructed image data, and adds at least one of the calculated bone characteristic indicators, the calculated bone density or bone mineral volume, and the calculated trabecular anisotropy by using a weight coefficient to calculate the bone strength indicator of the subject.
This invention relates to medical image processing for assessing bone strength in a subject. The system includes a hardware processor that reconstructs image data from medical imaging scans, such as CT or MRI, to generate a three-dimensional representation of the subject's bone structure. The processor analyzes this data to calculate bone characteristic indicators, such as bone density or bone mineral volume, which are quantitative measures of bone material properties. Additionally, the processor computes trabecular anisotropy, a measure of the directional variation in the trabecular bone structure, which influences bone strength. The system then combines these calculated indicators—bone characteristics, density/mineral volume, and trabecular anisotropy—using weighted coefficients to derive a comprehensive bone strength indicator. The weighted combination allows for a more accurate assessment of bone fragility or resistance to fracture by accounting for multiple structural and material factors. This approach improves upon traditional methods that rely solely on density measurements, providing a more nuanced evaluation of bone health. The invention is particularly useful in clinical settings for diagnosing osteoporosis or assessing fracture risk in patients.
6. The medical image processing device according to claim 5 , wherein the hardware processor calculates, as the trabecular anisotropy, a ratio between signal values of two pieces of small-angle scattering image data as the reconstructed image data in which a grating slit direction is set as a parallel direction and an orthogonal direction of a bone load direction of the subject.
This invention relates to medical image processing for analyzing trabecular bone structure, specifically focusing on quantifying trabecular anisotropy. The technology addresses the challenge of accurately assessing bone quality by leveraging small-angle scattering imaging techniques to evaluate the directional properties of trabecular bone. The device includes a hardware processor that computes trabecular anisotropy by analyzing two sets of small-angle scattering image data. The first dataset is reconstructed with the grating slit direction aligned parallel to the bone load direction of the subject, while the second dataset is reconstructed with the grating slit direction orthogonal to the bone load direction. The processor calculates the ratio between signal values from these two datasets to quantify anisotropy, providing insights into the structural organization and mechanical properties of trabecular bone. This approach enhances the ability to assess bone fragility and fracture risk by capturing directional variations in bone microstructure, which are critical for understanding bone strength beyond traditional density measurements. The method improves diagnostic accuracy in conditions like osteoporosis by offering a more comprehensive evaluation of bone quality.
7. The medical image processing device according to claim 1 , wherein the hardware processor corrects a signal value of the reconstructed image data or the bone characteristic indicator such that an angle of a grating slit direction with respect to the subject when photographing the moiré image data comes to an angle with respect to a bone load direction of the subject.
This invention relates to medical image processing, specifically for analyzing moiré images to assess bone characteristics. The technology addresses the challenge of accurately determining bone properties, such as density or structural integrity, from moiré fringe patterns generated during imaging. Moiré imaging involves capturing interference patterns created by gratings and the subject's bone structure, which can reveal mechanical properties under load. However, the accuracy of these measurements depends on the alignment between the grating slit direction and the bone's load direction during imaging. The device includes a hardware processor that reconstructs image data from moiré fringe patterns and calculates bone characteristic indicators, such as stress distribution or stiffness. A key feature is the processor's ability to correct signal values in the reconstructed image or the bone characteristic indicators to account for misalignment between the grating slit direction and the bone's load direction during imaging. This correction ensures that the derived bone properties accurately reflect the subject's true mechanical behavior, improving diagnostic reliability. The processor may adjust the signal values based on known relationships between the grating angle and the bone load direction, compensating for deviations to maintain measurement accuracy. This correction step is essential for applications like osteoporosis diagnosis or fracture risk assessment, where precise bone property evaluation is critical. The invention enhances the robustness of moiré-based bone imaging by mitigating alignment-related errors.
8. The medical image processing device according to claim 1 , wherein the hardware processor calculates the trabecular connectivity by using differential phase image data as the reconstructed image data.
This invention relates to medical image processing, specifically for analyzing trabecular bone structure in medical imaging. The problem addressed is the need for accurate assessment of trabecular connectivity, which is crucial for diagnosing and monitoring bone diseases like osteoporosis. Traditional methods often lack precision due to limitations in image reconstruction techniques. The device includes a hardware processor that processes medical image data to reconstruct images of trabecular bone. The processor calculates trabecular connectivity, a measure of the structural integrity of the bone's microscopic network. To improve accuracy, the processor uses differential phase image data as the input for this calculation. Differential phase imaging enhances contrast and resolution, particularly in soft tissue and fine bone structures, making it more effective for analyzing trabecular patterns compared to conventional intensity-based imaging. The hardware processor may also perform additional preprocessing steps, such as noise reduction and artifact correction, to ensure high-quality input data. The trabecular connectivity metric is derived from the processed differential phase images, providing a quantitative measure of bone health. This metric can be used to assess fracture risk, monitor treatment efficacy, or diagnose bone disorders. The use of differential phase imaging improves the reliability and sensitivity of the connectivity analysis, addressing limitations in prior art that relied on less precise imaging modalities.
9. The medical image processing device according to claim 1 , wherein the hardware processor calculates the trabecular number based on pixel number exceeding a prescribed threshold value on a profile of differential phase image data as the reconstructed image data.
This invention relates to medical image processing, specifically for analyzing trabecular bone structure in medical imaging. The technology addresses the challenge of accurately quantifying trabecular number—a key indicator of bone health—in high-resolution medical images, such as those obtained from phase-contrast imaging techniques. The device processes reconstructed image data, particularly differential phase images, to extract structural details of trabecular bone. The hardware processor analyzes the profile of the differential phase image data to identify pixels exceeding a prescribed threshold value. These pixels represent significant structural features of the trabecular bone. By counting these pixels, the processor calculates the trabecular number, which quantifies the density and spacing of trabeculae within the bone. This measurement is critical for diagnosing conditions like osteoporosis or assessing bone quality. The system leverages differential phase imaging, which enhances contrast in soft tissue and bone microstructures, enabling precise detection of trabecular features. The threshold-based approach ensures robustness against noise and artifacts, improving the reliability of trabecular number calculations. This method provides a non-invasive, high-precision tool for clinical and research applications in bone health assessment.
10. The medical image processing device according to claim 1 , wherein the hardware processor performs a frequency analysis of a profile of differential phase image data as the reconstructed image data and calculates, as the trabecular width, a length corresponding to a spatial frequency of the highest spectrum intensity within the spatial frequency corresponding to a trabecula.
This invention relates to medical image processing, specifically for analyzing trabecular bone structure in differential phase contrast imaging. The technology addresses the challenge of accurately quantifying trabecular width—a key indicator of bone health and fracture risk—from high-resolution medical images. Traditional methods often struggle with noise and artifacts, leading to inaccurate measurements. The device includes a hardware processor that performs frequency analysis on the profile of differential phase image data. This analysis identifies the spatial frequency corresponding to the trabecula, a key structural component of bone. The processor then calculates the trabecular width by determining the length associated with the highest spectrum intensity within this spatial frequency range. This approach leverages frequency-domain analysis to enhance measurement precision, reducing errors from image noise or artifacts. The method is particularly useful in medical imaging applications where detailed bone microarchitecture assessment is required, such as in osteoporosis diagnosis or treatment monitoring. By automating the trabecular width calculation, the device provides a reliable, objective metric for clinical evaluation.
11. The medical image processing device according to claim 1 , wherein the hardware processor calculates at least one of the trabecular connectivity, the trabecular number, and the trabecular width by using composite differential phase image data acquired by squaring and adding two pieces of differential phase image data as the reconstructed image data whose angles of grating slit directions with respect to the subject are different from each other by 90°.
This invention relates to medical image processing for analyzing bone microarchitecture, specifically trabecular bone structure, using differential phase contrast imaging. The technology addresses the challenge of accurately quantifying trabecular connectivity, trabecular number, and trabecular width from medical images, which are critical for assessing bone quality and diagnosing conditions like osteoporosis. The system processes composite differential phase image data derived from two differential phase images. These images are reconstructed from measurements where the grating slit directions are oriented at 90° angles relative to each other. By squaring and adding these two differential phase images, the system generates composite data that enhances the visibility of trabecular structures. A hardware processor then analyzes this composite data to compute at least one of trabecular connectivity, trabecular number, or trabecular width. Trabecular connectivity refers to the structural links between trabeculae, trabecular number measures the density of trabeculae per unit length, and trabecular width quantifies the thickness of individual trabeculae. This approach improves the accuracy of bone microarchitecture analysis by leveraging orthogonal differential phase imaging to capture detailed structural information that would be less discernible in conventional imaging methods. The technique enables better assessment of bone fragility and fracture risk, supporting clinical decision-making in musculoskeletal health.
12. The medical image processing device according to claim 1 , wherein the hardware processor calculates the mineralization degree by using composite small-angle scattering image data acquired by squaring and adding two pieces of small-angle scattering image data as the reconstructed image data whose angles of grating slit directions with respect to the subject are different from each other by 90°.
This invention relates to medical image processing for analyzing mineralization in biological tissues using small-angle scattering imaging. The technology addresses the challenge of accurately quantifying mineralization, such as in bone or calcified tissues, by leveraging small-angle scattering data to enhance image clarity and measurement precision. The device includes a hardware processor that processes small-angle scattering image data to calculate the mineralization degree. The key innovation involves generating composite small-angle scattering image data by squaring and adding two separate small-angle scattering images. These images are reconstructed from data where the grating slit directions relative to the subject differ by 90 degrees. This orthogonal acquisition and combination technique improves signal-to-noise ratio and reduces artifacts, enabling more accurate mineralization assessment. The hardware processor performs the mathematical operations to combine the orthogonal images, then analyzes the resulting composite data to determine the mineralization degree. This approach enhances the reliability of mineralization measurements in medical imaging applications, particularly for diagnosing or monitoring conditions like osteoporosis or vascular calcification. The method ensures robust data processing by leveraging orthogonal scattering patterns to mitigate directional biases in the measurements.
13. The medical image processing device according to claim 1 , wherein the hardware processor calculates, as the mineralization degree, an average signal value of small-angle scattering image data as the reconstructed image data.
The medical image processing device is designed for analyzing mineralization in biological tissues using small-angle scattering imaging techniques. The device addresses the challenge of accurately quantifying mineralization, which is critical for diagnosing and monitoring conditions like osteoporosis, atherosclerosis, and dental health. The hardware processor in the device processes small-angle scattering image data to generate a reconstructed image, which is then analyzed to determine the mineralization degree. Specifically, the processor calculates the average signal value of the small-angle scattering image data as the mineralization degree. This approach provides a quantitative measure of mineral content in the tissue, enabling precise assessment of mineralization levels. The device may also include additional features such as image reconstruction from raw scattering data, noise reduction, and visualization tools to enhance diagnostic accuracy. By leveraging small-angle scattering imaging, the device offers a non-invasive and highly sensitive method for evaluating tissue mineralization, improving early detection and treatment monitoring of mineral-related disorders.
14. The medical image processing device according to claim 12 , wherein the hardware processor performs correction by multiplying a correction coefficient that is calculated by using absorption image data as the reconstructed image data on the mineralization degree acquired from the small-angle scattering image data.
This invention relates to medical image processing, specifically for enhancing image quality in imaging systems that capture both absorption and small-angle scattering data, such as dual-energy X-ray imaging or phase-contrast imaging. The problem addressed is the need to improve the accuracy of mineralization degree measurements in medical images, particularly in applications like bone density analysis or tissue characterization, where artifacts or noise in the absorption data can lead to inaccurate results. The device includes a hardware processor that processes reconstructed image data derived from small-angle scattering image data. The processor applies a correction to the mineralization degree measurements by multiplying a correction coefficient. This coefficient is calculated using absorption image data as the reconstructed image data, ensuring that the mineralization degree values are adjusted based on the absorption characteristics of the scanned tissue. This correction helps mitigate discrepancies between the scattering-based mineralization measurements and the absorption-based density measurements, improving overall diagnostic accuracy. The system may also include a data acquisition unit for capturing the absorption and scattering data, an image reconstruction unit for generating the reconstructed images, and a display unit for presenting the corrected mineralization degree information. The correction process ensures that the final output is more reliable for clinical applications, such as osteoporosis diagnosis or tumor detection, where precise mineralization assessment is critical.
15. The medical image processing device according to claim 1 , wherein the hardware processor calculates the bone characteristic indicator for each of a plurality of small regions acquired by dividing a measurement target region of the reconstructed image data, and calculates a statistic of a plurality of calculated bone characteristic indicators.
This invention relates to medical image processing, specifically for analyzing bone characteristics in reconstructed image data. The problem addressed is the need for precise and localized assessment of bone properties, which is crucial for diagnosing conditions like osteoporosis or fractures. The device includes a hardware processor that processes medical image data, such as CT or MRI scans, to reconstruct a 3D image of a bone. The processor then divides the measurement target region of this reconstructed image into multiple small regions. For each small region, the processor calculates a bone characteristic indicator, which may include metrics like bone density, porosity, or structural integrity. The processor then computes a statistical measure, such as an average or standard deviation, of these indicators across all small regions. This approach provides a detailed and localized analysis of bone properties, improving diagnostic accuracy. The invention enhances traditional bone imaging by offering granular insights into bone health, which is particularly valuable for early detection of bone-related diseases. The hardware-based processing ensures efficiency and reliability in clinical settings.
16. The medical image processing device according to claim 1 , wherein the hardware processor calculates, as the osteoid volume, a signal integrated value or an average value of signal values in a trabecular region of a difference image data between a phase image data acquired by performing integration processing on differential phase image data as the reconstructed image data and absorption image data as the reconstructed image data.
This invention relates to medical image processing, specifically for analyzing bone structure using phase-contrast imaging techniques. The technology addresses the challenge of accurately quantifying osteoid volume—a key indicator of bone health—in medical imaging. Osteoid is the unmineralized organic component of bone, and its measurement is crucial for diagnosing and monitoring bone disorders. The device processes medical image data to generate a difference image by combining phase image data and absorption image data. The phase image data is derived from differential phase contrast imaging, which enhances sensitivity to subtle structural variations in bone. The absorption image data provides complementary information on tissue density. By integrating or averaging signal values within the trabecular (spongy) bone region of this difference image, the device calculates the osteoid volume. This approach improves the accuracy of bone structure analysis by leveraging the complementary strengths of phase-contrast and absorption imaging. The hardware processor performs integration or averaging of signal values in the trabecular region to derive the osteoid volume. This method enhances the detection of fine bone structures that may be missed by conventional imaging techniques. The device is designed to improve diagnostic precision in bone health assessments, particularly for conditions affecting bone mineralization and trabecular architecture.
17. The medical image processing device according to claim 1 , wherein the hardware processor acquires the cortical width from a profile including a cortical bone and a cancellous bone of the reconstructed image data.
This invention relates to medical image processing, specifically for analyzing bone structures in reconstructed image data. The device processes medical images to extract and measure the cortical width of bones, which is the thickness of the dense outer layer of bone. The cortical width is derived from a profile that includes both the cortical bone and the underlying cancellous (spongy) bone. The device uses hardware processing to accurately determine this measurement, which is critical for diagnosing bone conditions such as osteoporosis, fractures, or other structural abnormalities. The system reconstructs image data from medical scans, such as CT or MRI, and analyzes the bone profile to isolate and measure the cortical width. This measurement helps assess bone strength and integrity, aiding in clinical decision-making. The hardware processor ensures precise and efficient computation, enabling real-time or near-real-time analysis. The invention addresses the need for accurate, automated bone measurements in medical imaging, reducing manual errors and improving diagnostic reliability. The device may also include additional features such as image enhancement, noise reduction, and segmentation to improve the accuracy of cortical width extraction. The system is designed for integration into existing medical imaging workflows, providing clinicians with quantitative data to support diagnosis and treatment planning.
18. The medical image processing device according to claim 1 , wherein the hardware processor acquires average signal intensities of measurement target regions at same positions inside cortical bones of small-angle scattering image data as the reconstructed image data and of absorption image data, and calculates a ratio between the two acquired average signal intensities as the cortical porosity.
This invention relates to medical image processing for assessing cortical bone porosity. The technology addresses the challenge of accurately quantifying cortical porosity, which is important for diagnosing and monitoring bone diseases like osteoporosis. The device processes small-angle scattering image data and absorption image data of cortical bones to derive porosity measurements. The hardware processor in the device analyzes measurement target regions at corresponding positions within the cortical bone in both image types. It calculates the average signal intensities for these regions in the small-angle scattering and absorption images. The processor then computes the ratio of these average intensities to determine the cortical porosity. This approach leverages the complementary information from both imaging modalities to provide a more accurate porosity assessment than either modality alone. The device may also include additional features such as generating reconstructed image data from projection data, correcting for artifacts, and displaying the processed images. The porosity calculation is based on the relationship between the scattering and absorption signals, which reflects the structural properties of the bone. This method improves diagnostic accuracy by providing a quantitative measure of bone quality beyond traditional density measurements. The invention is particularly useful in clinical settings where precise bone health assessments are needed.
19. A medical image photographing system, comprising: the medical image processing device according to claim 1 ; and a medical image photographing device that photographs the subject and generates the moiré image data, wherein the hardware processor generates reconstructed image data from the moiré image data.
This invention relates to medical imaging systems designed to reduce moiré artifacts in captured images. Moiré patterns, which appear as unwanted interference fringes, often occur when imaging periodic structures like biological tissues or medical devices. The system addresses this issue by using a medical image photographing device to capture a subject and generate moiré image data. A medical image processing device then processes this data to reconstruct a high-quality image free of moiré artifacts. The processing device includes a hardware processor that applies a reconstruction algorithm to the moiré image data, converting it into clear, artifact-free reconstructed image data. The system may also include a display for showing the reconstructed image and a storage unit for saving the processed data. The hardware processor may further analyze the reconstructed image to detect specific features, such as lesions or anatomical structures, enhancing diagnostic accuracy. The invention improves medical imaging by eliminating distortions that could obscure critical details, making it particularly useful in fields like radiology, dermatology, and pathology where precise image clarity is essential.
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July 17, 2019
February 8, 2022
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